This disclosure relates to systems and methods for multiple-criteria decision-making in the domain of government incentivization of private sector investment in biomedical innovation. More specifically, the invention relates to simulation of the complex system consisting of the biomedical innovation pipeline and related entities and processes, as well as interactive methods for multi-objective optimization to support a satisficing decision process using the results of the simulation. These systems and methods can be used to integrate pertinent information sources to simulate public and private sector interactions and to facilitate decision-making that satisfices multiple competing objectives in the field of biomedical innovation.
Organizations engage in research and development (R&D) in an attempt to spur innovation in the various fields in which they are engaged, while at the same time maximizing value to the organization.
Currently, much of R&D planning involves private institutions and private sector organizations maximizing the expected net present value (NPV) (i.e., the net sum of funds being expended and received) and other financial metrics of their R&D portfolio through selecting and scheduling projects. Private sector organizations can also consider time, money, risk, resource constraints and project interactions (e.g. dependencies on the success of precedent projects) when determining which research projects to pursue. In the field of public health, the planning of R&D projects may need to take into account other factors that may not be present in other fields. For instance, public health research and development projects may need to take into account public health influences such as Disability Adjusted Life Years (DALY) burden relief, and public health spending reductions that may be implemented by the government. However, often times these factors are given little weight or are not considered at all.
Governmental organizations, through the implementation of policy, distribution of funds, and other incentivizing activities, can often impact private sector research. For example, U.S. Department of Health and Human Services (HHS) policy decisions (funding of basic and applied research, regulation of new biomedical innovations (e.g. parallel review for breakthrough drugs), payment policies for new biomedical innovations, etc.) may encourage private sector research in specific biomedical areas and conditions, including but not limited to biomedical innovations such as drugs, biologics, vaccines, diagnostics, and devices. However, the government organization trying to impact research and development may not be able to anticipate the overall response of the private R&D industry to those policies, or the potential impact on DALY burden relief and public health spending reductions. The impacts of current or planned policies by other government agencies such as tax incentives and by non-governmental organizations such as disease foundation sponsored research are also rarely considered in an integrated manner.
Multiple-criteria decision-making can be essential to the effective operation of many organizations. Decision-makers often implicitly, if not explicitly, attempt to simultaneously balance multiple competing requirements and goals. This is made more challenging when the outcomes of decisions are not obvious or easily predictable.
The biomedical innovation ecosystem—that is, the entities compromising, influencing, and influenced by the biomedical innovation pipeline, and the relationships between them—can be considered a complex adaptive system, characterized by complex and nonlinear interactions and interdependencies. Government policies can shift with new administrations or changes in budget, and policies of different government agencies can have an effect on one another, e.g. National Institutes of Health (NIH) research funding and the U.S. Food and Drug Administration (FDA) approval process may influence drug development, which will in turn affect disease prevalence and health outcomes, which are relevant to HHS, as well as Centers for Medicare & Medicaid Services (CMS) healthcare costs. The response of pharmaceutical companies and private sector organizations to changes in government funding and incentives is difficult to predict, and the magnitude of their response may not be proportional to the magnitude of policy change.
Multiple-criteria decision-making with regard to the biomedical innovation ecosystem can pose many challenges. One challenge is the complexity of the domain. For instance, risk and uncertainty can make it difficult to predict private sector response to new policies, the outcome of research or clinical trials for new biomedical innovations, how widely the innovations will be adopted if successful, the future presence of competing technologies, or the resulting health outcomes. Complex interactions between entities, interdependencies in their behavior, and nonlinear relationships between input parameters and outcomes further exacerbate these problems.
Another challenge faced in multiple-criteria decision-making with regard to the biomedical innovation ecosystem is computational intractability. In particular, the decision space of potential sets of policy decisions and budget allocations is too large to permit an exhaustive search of all possibilities. Therefore, algorithms are needed to cleverly traverse the space in search of policy options with desirable outcomes.
Even with the use of an efficient optimization algorithm, a further challenge of multiple-criteria decision-making with regard to the biomedical innovation ecosystem lies in the need for subjectivity in balancing competing objectives. In multi-objective optimization, there is often no single solution which is optimal with respect to all objectives, thus requiring subjective trade-off decisions. Such decisions may be intuitive for a human decision-maker, but may be difficult to translate into mathematical language that an automated algorithm can utilize.
Thus, a system that can simulate the biomedical innovation ecosystem and perform multi-objective optimization in support of multiple-criteria decision-making can prove to be valuable.